Λ Τͺg\γσ‚—ddlmZddlmZddlmZddlmZGd„de«Zdd„Z dd„Z dd „Z dd „Z dd „Z dd „Zgd ’Zy)ι)Ϊ annotations)ΪAny)ΪExpr)Ϊflattencσ —eZdZy)ΪSelectorN)Ϊ__name__Ϊ __module__Ϊ __qualname__©σϊG/var/www/openai/venv/lib/python3.12/site-packages/narwhals/selectors.pyrr s…r rcσ ‡—tˆfd„«S)uSelect columns based on their dtype. Arguments: dtypes: one or data types to select Returns: A new expression. Examples: >>> import narwhals as nw >>> import narwhals.selectors as ncs >>> import pandas as pd >>> import polars as pl >>> >>> data = {"a": [1, 2], "b": ["x", "y"], "c": [4.1, 2.3]} >>> df_pd = pd.DataFrame(data) >>> df_pl = pl.DataFrame(data) Let's define a dataframe-agnostic function to select int64 and float64 dtypes and multiplies each value by 2: >>> @nw.narwhalify ... def func(df): ... return df.select(ncs.by_dtype(nw.Int64, nw.Float64) * 2) We can then pass either pandas or Polars dataframes: >>> func(df_pd) a c 0 2 8.2 1 4 4.6 >>> func(df_pl) shape: (2, 2) β”Œβ”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β” β”‚ a ┆ c β”‚ β”‚ --- ┆ --- β”‚ β”‚ i64 ┆ f64 β”‚ β•žβ•β•β•β•β•β•ͺ═════║ β”‚ 2 ┆ 8.2 β”‚ β”‚ 4 ┆ 4.6 β”‚ β””β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”˜ cσL•—|jjt‰««S©N)Ϊ selectorsΪby_dtyper)ΪplxΪdtypess €rΪzby_dtype..7sψ€ § ‘ Χ 6Ρ 6΄wΈv³Τ Gr ©r)rs`rrr sψ€τV ΣGΣ HΠHr cσ—td„«S)u5Select numeric columns. Returns: A new expression. Examples: >>> import narwhals as nw >>> import narwhals.selectors as ncs >>> import pandas as pd >>> import polars as pl >>> >>> data = {"a": [1, 2], "b": ["x", "y"], "c": [4.1, 2.3]} >>> df_pd = pd.DataFrame(data) >>> df_pl = pl.DataFrame(data) Let's define a dataframe-agnostic function to select numeric dtypes and multiplies each value by 2: >>> @nw.narwhalify ... def func(df): ... return df.select(ncs.numeric() * 2) We can then pass either pandas or Polars dataframes: >>> func(df_pd) a c 0 2 8.2 1 4 4.6 >>> func(df_pl) shape: (2, 2) β”Œβ”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β” β”‚ a ┆ c β”‚ β”‚ --- ┆ --- β”‚ β”‚ i64 ┆ f64 β”‚ β•žβ•β•β•β•β•β•ͺ═════║ β”‚ 2 ┆ 8.2 β”‚ β”‚ 4 ┆ 4.6 β”‚ β””β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”˜ cσ6—|jj«Sr)rΪnumeric©rs rrznumeric..bσ€ § ‘ Χ 5Ρ 5Τ 7r rr r rrr:σ€τP Ρ7Σ 8Π8r cσ—td„«S)uΠSelect boolean columns. Returns: A new expression. Examples: >>> import narwhals as nw >>> import narwhals.selectors as ncs >>> import pandas as pd >>> import polars as pl >>> >>> data = {"a": [1, 2], "b": ["x", "y"], "c": [False, True]} >>> df_pd = pd.DataFrame(data) >>> df_pl = pl.DataFrame(data) Let's define a dataframe-agnostic function to select boolean dtypes: >>> @nw.narwhalify ... def func(df): ... return df.select(ncs.boolean()) We can then pass either pandas or Polars dataframes: >>> func(df_pd) c 0 False 1 True >>> func(df_pl) shape: (2, 1) β”Œβ”€β”€β”€β”€β”€β”€β”€β” β”‚ c β”‚ β”‚ --- β”‚ β”‚ bool β”‚ β•žβ•β•β•β•β•β•β•β•‘ β”‚ false β”‚ β”‚ true β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”˜ cσ6—|jj«Sr)rΪbooleanrs rrzboolean..rr rr r rr r err cσ—td„«S)u₯Select string columns. Returns: A new expression. Examples: >>> import narwhals as nw >>> import narwhals.selectors as ncs >>> import pandas as pd >>> import polars as pl >>> >>> data = {"a": [1, 2], "b": ["x", "y"], "c": [False, True]} >>> df_pd = pd.DataFrame(data) >>> df_pl = pl.DataFrame(data) Let's define a dataframe-agnostic function to select string dtypes: >>> @nw.narwhalify ... def func(df): ... return df.select(ncs.string()) We can then pass either pandas or Polars dataframes: >>> func(df_pd) b 0 x 1 y >>> func(df_pl) shape: (2, 1) β”Œβ”€β”€β”€β”€β”€β” β”‚ b β”‚ β”‚ --- β”‚ β”‚ str β”‚ β•žβ•β•β•β•β•β•‘ β”‚ x β”‚ β”‚ y β”‚ β””β”€β”€β”€β”€β”€β”˜ cσ6—|jj«Sr)rΪstringrs rrzstring..Έs€ § ‘ Χ 4Ρ 4Τ 6r rr r rr#r#s€τP Ρ6Σ 7Π7r cσ—td„«S)uρSelect categorical columns. Returns: A new expression. Examples: >>> import narwhals as nw >>> import narwhals.selectors as ncs >>> import pandas as pd >>> import polars as pl >>> >>> data = {"a": [1, 2], "b": ["x", "y"], "c": [False, True]} >>> df_pd = pd.DataFrame(data).astype({"b": "category"}) >>> df_pl = pl.DataFrame(data, schema_overrides={"b": pl.Categorical}) Let's define a dataframe-agnostic function to select string dtypes: >>> @nw.narwhalify ... def func(df): ... return df.select(ncs.categorical()) We can then pass either pandas or Polars dataframes: >>> func(df_pd) b 0 x 1 y >>> func(df_pl) shape: (2, 1) β”Œβ”€β”€β”€β”€β”€β” β”‚ b β”‚ β”‚ --- β”‚ β”‚ cat β”‚ β•žβ•β•β•β•β•β•‘ β”‚ x β”‚ β”‚ y β”‚ β””β”€β”€β”€β”€β”€β”˜ cσ6—|jj«Sr)rΪ categoricalrs rrzcategorical..γs€ § ‘ Χ 9Ρ 9Τ ;r rr r rr&r&»s€τP Ρ;Σ <Π>> import narwhals as nw >>> import narwhals.selectors as ncs >>> import pandas as pd >>> import polars as pl >>> >>> data = {"a": [1, 2], "b": ["x", "y"], "c": [False, True]} >>> df_pd = pd.DataFrame(data).astype({"b": "category"}) >>> df_pl = pl.DataFrame(data, schema_overrides={"b": pl.Categorical}) Let's define a dataframe-agnostic function to select string dtypes: >>> @nw.narwhalify ... def func(df): ... return df.select(ncs.all()) We can then pass either pandas or Polars dataframes: >>> func(df_pd) a b c 0 1 x False 1 2 y True >>> func(df_pl) shape: (2, 3) β”Œβ”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β” β”‚ a ┆ b ┆ c β”‚ β”‚ --- ┆ --- ┆ --- β”‚ β”‚ i64 ┆ cat ┆ bool β”‚ β•žβ•β•β•β•β•β•ͺ═════β•ͺ═══════║ β”‚ 1 ┆ x ┆ false β”‚ β”‚ 2 ┆ y ┆ true β”‚ β””β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β”˜ cσ6—|jj«Sr)rΪallrs rrzall..s€ § ‘ Χ 1Ρ 1Τ 3r rr r rr)r)ζs€τP Ρ3Σ 4Π4r )r)r rr&rr#N)rrΪreturnr)r*r)Ϊ __future__rΪtypingrΪ narwhals.exprrΪnarwhals.utilsrrrrr r#r&r)Ϊ__all__r r rΪr0sGπέ"εεέ"τˆtΤσ+Iσ\(9σV(9σV(8σV(=σV(5ςV r